PyVideo.org - Python

A Python Takeover

2011-07-24T00:00:00+00:00

Summary

[EuroPython 2011] Mark Ramm-Christensen - 23 June 2011 in "Track Lasagne"

Description

Two years ago I started working on SourceForge with a couple of python developers. Back then the site was written in PHP and was in some ways buried in technical debt. Everything is different today, we have a complete set of tools for Ticket tracking, Wiki documents, Git, Mercurial, Subversion, and IRC - all written in Python, and now released as open source software.

This is the story of how the amazing libraries, tools, and talent in the Python web community has radically changed an established company, and allowed us to reinvent sourceforge.net.

This talk will go over our tech stack, talk about the libraries we used, the way we solved scalability, internationalization, and performance issues issues. Ultimately we know that without python, we could not have built a flexible plugin based system, and gotten permission from management to open source it.

Django productivity tips and tricks

2011-07-24T00:00:00+00:00

Summary

[EuroPython 2011] Simone Federici - 23 June 2011 in "Track Ravioli"

Description

I will show in this talk some tips, tricks and best practices for some recurring patterns in the django application development.

This is not a list of tools and applications, but rather a homemade summary of code snippets and design best practices. All in all, the goal is: no mistakes and go faster.

Topics include: python, users, forms, jquery, virtualenv, distribute, buildbot, etc. …

Snakes on a cloud: the OpenStack project

2011-07-24T00:00:00+00:00

Summary

[EuroPython 2011] Thierry Carrez - 23 June 2011 in "Track Lasagne"

Description

OpenStack is an innovative open source project written in Python, backed by Rackspace Hosting and NASA, building a massively-scalable and reliable cloud computing platform.

The first part of this talk will clarify the place of OpenStack in the general "cloud" landscape and explain why a fully open cloud infrastructure stack is necessary to avoid vendor lock-in. We'll then focus on the OpenStack project goals, its developer community, its open design and release processes, and the developer tools it chose.

The second part of the talk will present into more technical details the different components of OpenStack: Nova (compute) and Swift (storage), including the Python libraries that are used (libvirt, SQLAlchemy, eventlet…). A Q&A session at the end of the talk will give the audience a chance to clear any remaining dark area.

Sqlkit: empowering database access

2011-07-24T00:00:00+00:00

Summary

[EuroPython 2011] Alessandro Dentella - 23 June 2011 in "Track Spaghetti"

Description

It's very easy to access databases with Python, and there are many ORMs allowing a high level of abstraction. Still, it's a long road from there to interactive handling of data, even for very simple tasks.

Sqlkit tries to act as a bridge, offering some mega-widgets that allow you to build GUI applications or directly access data using the 'sqledit' application, shipped with sqlkit; sqlkit is built with PyGTK and SQLAlchemy.

This presentation covers some of the most powerful features of sqlkit, and shows how easy it is to build small programs for personal use, as well as rich and complex applications. It primarily focuses on two points: how easy it is to create data forms (eventually with one2many or many2many, thanks to a original definition of layouts), and the template system based on OpenOffice which allows easy report creation.

This presentation shows how sqlkit can be used both as application development framework and as a tool to help us while developing other applications (e.g.: Django, OpenERP,… ).

To deliver a gradual presentation, we'll use the configuration system of sqledit, starting from a minimal configuration (a single URL) we'll add elements to that until we'll have a full application.

Exploit your GPU power with PyCUDA (and friends)

2011-07-21T00:00:00+00:00

Summary

[EuroPython 2011] Stefano Brilli - 22 June 2011 in "Track Spaghetti"

Description

CUDA technology permits to exploit the power of modern NVIDIA GPUs. In this talk, after a brief introduction to GPU architecture, we will focus on how CUDA got inside Python through libraries like PyCUDA and others…

By some examples we will show the main concepts and techniques for good GPU programming.

This talk targets anyone who wants to know how to exploit this technology from Python, the suitable use cases, the using techniques and the do-not-using techniques to get the best from his own GPU

Debugging and profiling techniques

2011-07-20T00:00:00+00:00

Summary

[EuroPython 2011] Giovanni Bajo - 22 June 2011 in "Track Spaghetti"

Description

This talk will give on overview over some common problems related to profiling and debugging CPython applications (especially desktop-based ones).

The following subjects will be covered:

Debugging of memory/resource leaks (circular references, del, weakref, garbage collector, etc.). We will show in details how the memory management of Python objects work and we will show a few tricks to track memory leaks
Python code profiling (profile/hotshots/cProfile, design of small tests, etc.). We will show how to measure, profile, and analyse an application to spot performance problems and solve them.
Post-mortem step-by-step debugging of C/C++ extension under Windows. We will show how to setup Visual Studio for debugging, how to see a traceback in case of an unexpected segfault, and how to further debug and solve the crash.

This talk is aimed at intermediate Python programmers, who have already developed non-trivial Python applications but have never "jumped through" the interpreter abstraction layer. If you feel a little lost when you see a segfault from a Python program or you don't have a clear idea how memory is collected in Python, this talk is perfect for you.

Django: trucchi per migliorare la produttività

2011-07-13T00:00:00+00:00

Summary

[EuroPython 2011] Simone Federici - 22 June 2011 in "Track Italiana Big Mac "

Description

Proverò ad illustrare in questi 60 minuti alcuni consigli e best practices, da utilizzare nello sviluppo di applicazioni django based.

Non è un mero elenco di tools e/o applicazioni, bensì un bignami di snippets e tips architetturali. Tutto sommato l’obbiettivo è non sbagliare e andare più veloci.

Argomenti trattati: python, users, forms, jquery, virtualenv, distribute, buildbot, ecc…

Meteorology, Climate and Python: desperately trying to forget technical details

2011-07-13T00:00:00+00:00

Summary

[EuroPython 2011] Claude Gibert - 24 June 2011 in "Track Lasagne"

Description

Python is a great language for writing programming frameworks. Python frameworks are normally addressed to software developers who are Python professionals. I developed a software package in a scientific institution, designed to be used by non-programmers, but also designed to enable customisation through programming by some users. I finally designed a three level package:

A Python programming framework, addressed whoever wanted to invest in Python and had plans to develop applications;
An application built on that framework with an interface designed for non-programmers;
Ways of customising the behaviour of the application by providing some basic Python functions or more elaborated code.

One of the challenges was to offer an application with an easy to use interface, not graphical, not web-based and not requiring Python programming. This interface was necessary for batch processing.

This talk addresses how this project was carried out, the technical solutions adopted and how Python was introduced in an operational scientific institution (http://www.ecmwf.int) where most users were Fortran programmers. Python was introduced as early as 2004 and it was a challenge to gain acceptance. I will also make a parallel with a project I am currently working on for NASA (http://gmao.gsfc.nasa.gov/). Desperately trying to forget technical details summarises how I tried, using Python, to help Meteorology scientists to focus on their domain of expertise instead of constantly solving technical problems.

Background:

The disciplines of Meteorology and Climate involve numerical modelling of physical phenomena. The amount of data going in and out of the model is considerable. The organisation and the storage of data is complicated, their post-processing is a challenge. Scientists need to access and process input and output data to monitor the trends of the input data and to evaluate the performance of their models. Those statistics, diagnostics, plots and verifications are crucial to the improvement of the quality of the models. Finding the right data, decoding it, transforming it to be ready for use are necessary steps to initiate the pre-processing. All these actions are fundamentally the same between different prediction centres, but the data organisation and file formats can differ.

Programmazione MapReduce in Python con Pydoop

2011-07-13T00:00:00+00:00

Summary

[EuroPython 2011] Simone Leo - 23 June 2011 in "Track Italiana Big Mac "

Description

Hadoop è la principale implementazione open source di MapReduce, il paradigma di calcolo distribuito su larga scala di Google. L'API nativa di Hadoop è in Java e le opzioni built-in per la programmazione in Python - Streaming e Jython - presentano diversi inconvenienti: la prima consente di accedere solo a un piccolo sottoinsieme delle funzionalità di Hadoop, mentre la seconda ha tutte le limitazioni di Jython rispetto a CPython.

Pydoop è un'API per Hadoop che rende disponibile buona parte delle funzionalità di Hadoop al programmatore Python, consentendo lo sviluppo in CPython. I suoi moduli di base sono wrapper Boost.Python per l'interfaccia C/C++ di Hadoop.

Il talk consiste in un tutorial su MapReduce/Hadoop e in una presentazione dell'API Pydoop, con l'obiettivo principale di avvicinare le community di Hadoop e Python. Può essere utile, anche se non strettamente necessaria, una conoscenza di base della programmazione distribuita.

Python 3: the Next Generation (is here already)

2011-07-13T00:00:00+00:00

Summary

[EuroPython 2011] wesley chun - 21 June 2011 in "Track Lasagne"

Description

Python is currently at a crossroads: Python 2 has taken it from a quiet word- of-mouth language to primetime, with many companies around the world using it and an ever-increasing global marketshare of the programming world. But now comes Python 3, the first version of the language that is not backwards compatible with previous releases.

What does this mean? Are all my Python programs going to break? Will I have to rewrite everything? How much time do I have? When is Python 2 going to be EOL'd? Is the language undergoing a complete rewrite and will I even recognize it? What are the changes between Python 2 and 3 anyway? Also, the next generation is already here, as Python 3 is over two years old now. What has been ported so far, and what is its current status? Are migration plans or transition tools available? If I want to start learning Python, should I do Python 2 or Python 3? Are all Python 2 books obsolete?

We will attempt to answer all of these questions and more. Join us!

OUTLINE/TOPICS

Python 2 vs. Python 3
Introduction to Python 3
Backwards Compatibility
Generational Changes
Key Differences
Role of Remaining Python 2.x releases
Transition & Migration Plans & Tools
Futures

Python for High Performance and Scientific Computing

2011-07-13T00:00:00+00:00

Summary

[EuroPython 2011] Andreas Schreiber - 23 June 2011 in "Track Lasagne"

Description

Python is an accepted high-level scripting language with a growing community in academia and industry. It is used in a lot of scientific applications in many different scientific fields and in more and more industries, for example, in engineering or life science). In all fields, the use of Python for high- performance and parallel computing is increasing. Several organizations and companies are providing tools or support for Python development. This includes libraries for scientific computing, parallel computing, and MPI. Python is also used on many core architectures and GPUs, for which specific Python interpreters are being developed. A related topic is the performance of the various interpreter and compiler implementations for Python. The talk gives an overview of Python’s use in HPC and Scientific Computing and gives information on many topics, such as Python on massively parallel systems, GPU programming with Python, scientific libraries in Python, and Python interpreter performance issues. The talk will include examples for scientific codes and applications from many domains.

Slides

Python(x,y): diving into scientific Python

2011-07-13T00:00:00+00:00

Summary

[EuroPython 2011] Vincent Noel - 22 June 2011 in "Training Pizza Margherita "

Description

This training session will introduce the Python scientific stack to beginner or intermediate-level Python programmers. The basics of scientific programming with Python will be presented:

creation of arrays and structured arrays using numpy
fast, loopless manipulation of numpy arrays through fancy indexing and vectorized functions
convenient saving/loading of array variables using numpy
improved interactive use through ipython
data analysis using various scipy modules (signal analysis, image classification, etc)
plotting large time series, histograms, scatterplots, images etc. using matplotlib
saving/loading large datasets in structured scientific formats such as netCDF, HDF (depending on interest)

These concepts will be used in coding exercises, in the programming environnement provided by the Python(x,y) distribution, which is freely downloadable and includes recent versions of Python, numpy and matplotlib. The Python(x,y) distribution runs on Windows, which will be the OS of choice for this session. You should still be able to follow the examples in other Python distributions, e.g. EPD.

The entire talk (PDF), code samples and data files can be downloaded from the following archive

Sfrutta la potenza della GPU con PyCUDA (e compagni)

2011-07-13T00:00:00+00:00

Summary

[EuroPython 2011] Stefano Brilli - 22 June 2011 in "Track Italiana Big Mac "

Description

CUDA è una tecnologia che permette di sfruttare la potenza di calcolo delle moderne schede video prodotte da NVIDIA. In questo talk, dopo una breve introduzione all'architettura della GPU, si vedrà come CUDA entra all'interno di Python attraverso librerie come PyCUDA e non solo…

Mediante alcuni esempi si metteranno in luce i concetti e le pratiche fondamentali per una buona programmazione della GPU.

Il talk è rivolto a chiunque voglia conoscere come sfruttare questa tecnologia all'interno di Python, i casi in cui è conveniente utilizzarla, le pratiche da adottare e le pratiche da non adottare per trarre il massimo dalla propria GPU.

Spotify and Python: love at first sight

2011-07-13T00:00:00+00:00

Summary

[EuroPython 2011] Jon Åslund - 20 June 2011 in "Track Spaghetti"

Description

This is a story about how Spotify started, how we evolved, where we are today and how Python has always been a big part of our success. Where do we use it? Where do we not use it? Learn about the Unix way and the Debian way of maintaining a healthy love for Python, while two-timing with other languages such as Java and C++.

Creare videogames con Panda3D

2011-07-07T00:00:00+00:00

Summary

[EuroPython 2011] Claudio Desideri - 21 June 2011 in "Track Italiana Big Mac"

Description

In questo talk spiegherò quali opportunità ci offre il game-engine Panda3D nel momento in cui vogliamo creare un videogioco da zero. Spiegherò com'è strutturato al suo interno e i concetti di base per capire come funziona, ma mostrerò anche prove pratiche e pezzi di codice.

Può partecipare chiunque abbia una conoscenza di base di Python, anche se non ha mai lavorato con applicazioni realtime 3D. L'obiettivo è mostrare come creare un semplice videogioco che risponda agli input immessi dall'utente e come utilizzare al meglio questo potente engine.

Python Performance: Past, Present and Future

2019-07-12T00:00:00+00:00

Description

Python Performance: Past, Present and Future

Python Standard Library, The Hidden Gems

2019-07-12T00:00:00+00:00

Description

The Python Standard Library is a very rich toolset, which is the reason why Python is stated to come with "batteries included". In such an amount of features and tools it's easy to get lost and miss some of the less unknown modules or gems hidden within the whole load of functions and classes.

This talk aims at showcasing some recipes, snippets and usages of standard library modules and functions that are less known or that are not covered in the most common books and tutorials available for Python.

The talk will try to showcase a bunch of short examples in the hope to foster the "oh, wow! I didn't think about that" reaction at least once in the audience.

We will see how frequently for tasks where you used third party libraries or frameworks a solution bultin into the standard library is already available, and such solution is guaranteed to be maintained and well working for the years to come thanks to the standard library reliability and stability.

The showcased examples are took from the “Modern Python Standard Library Cookbook” book that I authored.

The soul of the beast

2019-07-12T00:00:00+00:00

Description

Why

The audience will discover one of the core pieces of the language that sits at the middle of the decisions about what new rules can or cannot be implemented in the Python programming language. They will learn how the particularities of the grammar limit what can be achieved but also serve to maintain the language consistent, powerful but straightforward. Attendants will learn how core developers solved some challenging scenarios that arise as a consequence of said limitations or how others cannot be resolved unless Python gets a significant transformation in the internal mechanism that parses the grammar. Finally, they will learn how a new rule is added to the CPython grammar, serving as a perfect example of how all the pieces come together. In summary, the audience will gain a more technical response to why people perceive the Python programming language as easy but powerful one and at the same time will gain some insight on how to understand and extend the pieces that form it. This talk will not only help members of the audience understand better the design of the language a how grammars and parser work, but will also help people wanting to contribute to CPython understanding the general structure of the compiler pipeline and how to work on it.

Who

This talk is for those that want to understand Python a bit deeper: not only how everything works under the hood but also what are the technical decisions in its making and what are the consequences. The talk is targeted to all Python programmers, no matter the skill level as everyone will find something for their particular level of expertise:

Beginner programmers will be introduced in the topic of language grammars and will learn what a Grammar is and what are the building blocks. Also, the audience members in this level will gain insight into how everything is thread together in CPython.
Medium and advanced programmers will learn some in-depth technical details and how they relate to features they already know and understand. The talk not only will try to enlight some new areas related to grammar technicalities, parser features and design and CPython implementation details but will also connect many pieces of information to explain how the small technical decisions impact the bigger picture.

Outline

Who am I

What is the Python Grammar

What is grammar?
How they look like.
Elements: terminal symbols, nonterminal symbols, productions.

The properties of Python Grammar?

Leftmost derivation
1 token lookahead
No epsilon productions! (Plus what epsilon productions are)
Some immediate consequences of these properties.

How the Python parser generator works

General structure of the parser generator.
Non Deterministic Finite Automata
Deterministic Finite Automata.
Some examples (with cool graphs!) generated from the python grammar and the parser generator of the actual finite automatas that Python uses.
Concrete syntax trees.

Advantages of the grammar (or "why Python is so easy to understand)

LL(1) grammars are context-free (no state to maintain while parsing).
LL(1) grammars are simple to implement and very fast to parse.
LL(1) grammars are very limited, keeping the language simple

Disadvantages of the grammar:

Grammar ambiguity.
LL(1) grammars need some hacks for very simple things.
How keyword arguments were incorporated in the grammar with a hack: The grammar rule is very strange because it is "fixed" in the Abstract syntax tree
Why parenthesized with statements cannot be implemented (with statements formed of multiple elements surrounded by parenthesis and separated by commas).

Implementing a new grammar rule in CPython: the arrow operator :

A complete mini-tutorial on how to introduce a new operator: A -> B that gets executed as A.rarrow(B).
Altering the grammar and generating the new parser.
Introducing a new token.
Changing the tokenizer.
Changing the Abstract Syntax Tree Generator.
Changing the compiler.
Implementing the new opcode.
Implementing the rarrow protocol.

The future and summary of the talk:

We have been discussing in the CPython discourse to change the parser generator to something more powerful.
Dangers and advantages of other parser generators.
What other implementations are using?
Summary of the talk

Advanced pytest

2019-07-11T00:00:00+00:00

Description

There are many reasons why I think pytest is a fantastic test framework: assertion introspection, setup and teardown with fixtures, parametrized tests. But plugins are the real game changer!

We'll dive right into customizing a test suite by developing a series of pytest plugins based on a few neat hacks. We'll learn how to automatically add markers to slow running tests, run only tests that use a fixture which can be specified on the CLI, generate test reports in a custom format, and automatically create GitHub issues for test regressions. We'll wrap up this talk by using Cookiecutter to create Python packages from our plugins that are ready to be published on PyPI.

Introduction to Python and MongoDB

2019-07-11T00:00:00+00:00

Description

This talk will give anyone who has a basic knowledge of Python insight into how

easy it is to build database applications using the PyMongo Python library for MongoDB. There will be live coding!

Testing Microservices: fast and with confidence

2019-07-11T00:00:00+00:00

Description

A main advantage of microservices is improved developer velocity. One roadblock to achieving it is giving developers the confidence that their changes are correct and safe, which is a challenging problem in such a distributed architecture. Typical approaches involve relying on automated end- to-end testing, which is costly to set up, develop tests for and run.

In this talk I will explore an approach to testing that does not require the presence of any external dependencies (not even "fake" or "test double" implementations of them), but provides many of the benefits of an end-to-end test. Come by to learn about how we can use a downstream service's API specification to make sure the system under test interacts with it in the correct way ("contract testing") - a key ingredient missing from most unit or integration test setups. We'll then go even further to cover testing scenarios that previously could only be covered with end-to-end tests: how to maintain and validate state of your downstream dependencies.

“When a biologist met Python”

2019-07-11T00:00:00+00:00

Description

Biology and computing are closer than we usually think, for example
many algorithms are inspired in biology patterns, and complementary to
that, researchers needs special algorithms to have a better
understanding of our environment. Thus, there is a strong relation an
dependency.
In the past years, Biology has been transformed into computational
biology. Therefore
technological advances helps us to predict physical interactions
between atoms and DNA, because we are being able to integrate
information from biology into algorithms.

Python has become a popular programming language in biosciences because it has a clean syntax that makes it easy to read language. In addition to this, there are many modules (toolkits) extending to different biological domains, like metabolomics, structure analysis, phylogenomics, molecular biology and others. Python is currently improving researcher’s workflow, helping us to focus on the theory or experimental part, instead of fighting with old buggy applications.

This talk aims to be oriented to all audiences (with/without biological background) since we will go together through an amazing adventure into the natural sciences using tools like Biopython, Bokeh, Networkx, Ecopy and much more! Are you brave enough to follow me on this journey?

Accelerate your Deep Learning Inferencing with the Intel® DL Boost technology

2019-07-10T00:00:00+00:00

Description

Learn about Intel® Deep Learning Boost, also known as Vector Neural Network Instructions (VNNI), a new set of AVX-512 instructions, that are designed to deliver significantly more efficient Deep Learning (Inference) acceleration. Through this technology, I will show you how you can perform low-precision (INT8) inference much faster on hardware that support the VNNI instruction set (for example, the 2nd generation Intel Xeon Scalable processors, codenamed, Cascade Lake). In the live Jupyter notebook session, you can will be able to see the benefits of this new hardware technology.

Note: This is an advanced talk. Knowledge about Deep Learning, Inferencing and basic awareness of hardware instruction sets would be desirable.

From days to minutes, from minutes to milliseconds with SQLAlchemy

2019-07-10T00:00:00+00:00

Description

Object Relational Mappers (ORMs) are awesome enhancers of developer productivity. The freedom of having the library write that SQL and give you back a useful, rich model instance (or a bunch of them) instead of just a tuple or a list of records is simply amazing.

But if you forget you have an actual database behind all that convenience, then it'll bite you back, usually when you've been in production for a while, after you've accumulated enough data that your once speedy application starts slowing down do a crawl.

Databases work best when you ask them once for (or to do) a bunch of stuff, instead of asking them lots of times for small stuff.

We'll discuss how innocent looking attribute accesses on your model instances translate to sequential queries (the infamous N+1 problem).

Then we'll go through some practical solutions, taken from real cases, that resulted in massive speed ups. We'll cover how changes in Python code resulted in changes to the resulting SQL Queries.

We'll see solutions not only for queries, but also for inserts and updates, which tend to be less well documented.

Though this talk focuses on SQLAlchemy, the lessons should be applicable to most ORMs in most programing languages. The ideas discussed, and solutions proposed are also valid for any storage back-end, not only SQL databases.

This talk is geared towards Python developers with systems that talk to databases. It should be accessible to anyone who already programs in Python (early intermediary level), but will be most useful for developers with projects talking to SQL databases, specially using an ORM like SQLAlchemy. Attendees will learn to detect how N+1 query situations arise and how to work around them effectively. They will also learn how to do mass inserts and mass updates with SQLAlchemy.

Python vs Rust for Simulation

2019-07-10T00:00:00+00:00

Description

Both Python and Rust are getting more and more popularity. Although it
is unfair to compare them directly, this talk aims to provide a clear
comparison with a pre-defined criteria applied to a specific use-case.
Writing a simulation engine is very similar to writing a game engine
and requires certain features from a language or framework.
Possible comparison criteria:
1. Performance
2. Simplicity
3. Amount of code necessary for a MVP
4. Utilities: docs, tests, profile
5. Compatibility

Tl;dr

Do I want to go back to Python after Rust? Maybe+)

Python's Parallel Programming Possibilities - 4 levels of concurrency

2019-07-10T00:00:00+00:00

Description

I'm going to talk about the 4 main levels of parallelism in modern Computing:

- multiple (virtual) machines
- multiple processes
- multiple threads
- multiple green threads, aka asyncio

Why you might use each of them, how to go about doing so with python and some of the pitfalls you might fall into along the way.

To do so, I'll give short examples in code of achieving each level:

- leveraging multiple hosts using RQ, and also the possibility of RPC
with HTTP
- multiprocessing and threading using their respective modules from
the python standard library
- asyncio demonstrated with AIOHTTP

That sounds great, but there are "gotchas" you should know about before you get started, for example:

- multiple machines can actually be multiple virtual machines on the
same host
- effectively communicating between processes is hard, how can we go
about making it easier?
- the limitations of threading and the GIL
- run_in_executor - do we ever really need to use multiprocessing or
threading directly again
- use of asyncio when dealing with both networking between hosts and
between processes - you end up using two different kinds of
concurrency at the same time. That can be confusing, but also awesome.

I'll finish of by showcasing a library I built, arq which is a job queueing and RPC library for python which uses asyncio and Redis.

Advanced Infrastructure Management in Kubernetes using Python

2020-07-23T00:00:00+00:00

Description

Automate managing complex applications in a cloud native way using Operators written in Python

Many of us are using Kubernetes in production. A Kubernetes Operator is a way to automate packaging, deploying, and managing of a Kubernetes Application. It is a software alternative to a human operator who has deep knowledge of how to set up, deploy, and manage a particular piece of infrastructure and what to do if it isn’t behaving correctly. Let’s see how we can automate all of this while staying in the Python ecosystem.

It will be helpful to know some basic concepts of Kubernetes(Deployments, Services, Pods, Configmap etc.) and Celery(docs.celeryproject.org) to get the most out of this talk.

Talk is divided into four phases.

Phase I - Problems and Opportunities We're going to see some simple examples/problems where a lot of manual effort is involved so as to connect audience to the problem. We're going to discuss problems with configuration management, database cluster setup and introduce the focus problem of the talk which is around automating the setup of a Celery cluster.

Phase II - Incrementally Approaching the Solution We're going to incrementally approach the automation each of the manual steps involved in running a Celery cluster in Production. We're going to discuss the extension capabilities in Kubernetes using CRDs and Custom Controllers which are going to help us manage our Celery cluster automagically.

Phase III - Celery Operator in action We're going to see the code of custom controller and the whole operator in action. We create the newly defined celery resource and see how the operator works on bringing up the worker and flower deployments and handles autoscaling based on queue length.

Phase IV - Conclusion and Q&A We're going to talk about different use-cases and what is world doing with Operators. We'll discuss the next steps for the Celery operator and some resources to help build operators. We'll end the talk with a Q&A.

Slides for the talk are available on - https://bit.ly/europython20-ppt Celery Operator POC I built for this talk is open source - https://github.com/brainbreaker/Celery-Kubernetes-Operator

Attractive GUIs with PySimpleGUI

2020-07-23T00:00:00+00:00

Description

With PySimpleGUI everyone can build powerful GUI applications without a steep learning curve.

In the Python landscape, there are several high-end GUI packages. Of course, there’s tkinter which is part of the standard library. And there is wxPython and several versions of Qt. What they share is that they are very flexible but not only have a steep learning curve but also take considerable development effort to realize even a simple user interface. At the other of the spectrum, there are several design tools that either generate code of a layout structure. In many cases, the functionality is rather limited, though. Relatively recent, an independent open-source developer (not me) has made a product that tries to bridge the gap between these two worlds: PySimpleGUI. This package runs on top of tkinter, QT for Windows, wxPython and Remi. In this talk, I want to show the design methodology behind this fascinating product that might change the way people look at GUIs. I will start with live coding a simple yet not evident program that shows the basic concepts. With this, I will demonstrate the design philosophy. PySimpleGUI can also be used to implement a user-friendly interface as opposed to command-line interfaces. From there, I will give an overview of some more elements present in this package. Finally, I will show a full-featured GUI program with several bells and whistles.

Automate your tasks with Python and publish with Chat Apps

2020-07-23T00:00:00+00:00

Description

That’s the way to build a Virtual Assistant for your team, the way to boost productivity.

The technology is to take script written in a high-level language (Python), analyze its inputs/outputs, images or graphs display, other interactions with the user and build connectors to this script from Chat Apps like Slack, Skype, and others. Imagine that you described your skill as a script and gave it to a software robot. And from that moment it is his skill, you and your colleagues can ask him any time to run this skill and give results. As a script author, you don't think about how to connect with different Chat Apps, how to control access, how to monitor that script works fine when colleagues run it, how to save logs, how to balance load if there are too many colleagues running script and so on, our platform does all of this so the author doesn't need to think about what is that software robot, how it works, the platform will understand script by itself. The author just drop script to the platform and tell to the platform which colleagues have the right to run this script.

For whom?

For teams who are in charge of Servers, Applications, API's, Data Bases, Analytical reports and other information technology items.

For tasks like

to fetch diagnostic information from multiple data sources into Chat App, gather metrics/logs and analyzing them

to take an action in case of incident right from a Chat App, rerouting users requests, server rebooting, launching new instances, and many other actions

to give easy access via Chat App to APIs for team members and other coleagues

to provide analytical reports by the request from Chat App, reports with graphs, images, tables, files, etc.

These things are trivial to automate and share, you can do much more with Python, Chat App and smart script sharing platform.

Deceptive Security using Python

2020-07-23T00:00:00+00:00

Description

Deceiving hackers to protect your resources

Imagine you are passing through an unknown street at midnight and you find that some anti-social elements are following you. To save yourself from them you start running and look for a safe place to hide. On the way, you will find a good person and requests him to help you. He hides you in his place to protect you. When these anti-social elements visit a good person’s place and enquire about you, the good person misguides them and redirects them to some other place in order to protect you. This is exactly how deception works. In this analogy, YOU are the resources to be protected, anti-social elements are the hackers who want to gain access to the resources, and a good person is a deception technique that protects the resources from hackers by making them fall in the trap.

The talk begins with an introduction to deception technology, deception types, and methods, a deceptive security life cycle. In this talk, we will demonstrate the following deception tools implemented using python language: • WebTrap (https://github.com/IllusiveNetworks-Labs/WebTrap): is designed to create deceptive webpages to deceive and redirect attackers away from real websites. The deceptive webpages are generated by cloning real websites, specifically their login pages. • DemonHunter (https://github.com/RevengeComing/DemonHunter): is a distributed low interaction honeypot with Agent/Master design Finally, we will conclude the talk with how built a deception tool and demonstrate its working.

How we implemented a deception tool in python using machine learning: We designed a deception tool in python language using PyBRAIN package to model and mitigate XPath injection attacks for web services. It is known that XML can be used to store the data and this data can be queried using XPath query language. XPath is a query language, it has injection issues similar to SQL. To handle this issue, we proposed a solution, which uses a count-based validation technique and Long Short-Term Memory (LSTM) modular neural networks to identify and classify atypical behavior in user input. Once the atypical user input is identified, the attacker is redirected to fake resources to protect the critical data. Our experiment resulted in over 90% accuracy in the classification of input vectors.

Outline 1. Introduction to deception, Deception types, Deception technology applicable methods and Deception Life cycle(08 Minutes) 2. Demonstration of WebTrap deception tool(04 Minutes) 3. Demonstration of DemonHunter deception tool(04 Minutes) 4. Discussion of our deception tool and demonstration(06 Minutes) 5. Conclusion and Questions(03 Minutes)

Audience No experience level of Python is needed. In general, anyone can attend this talk and learn about applying deception techniques and machine learning to application security.

Elegant Exception Handling

2020-07-23T00:00:00+00:00

Description

Error handling is hard. Regardless of the approach you take, it usually means littering your application with checks and validations that greatly reduce code readability. So how can we tackle exceptions?

Honey, There Is a Python in My Android Phone!

2020-07-23T00:00:00+00:00

Description

This project was initiated during my Christmas vacation, inspired by Android Barcode Scanner as I was looking on the solution that I could catalogue my books by scanning the ISBN using android phone. This quick solution did save me enormous amount of time in cataloguing my books. Intriguing by the design of APIs that can interact with android intents, I decided to dig further to understand how the APIs works. In concept, there is an RPC call between android app and python interpreter within the app that acts as a bridge, which allows python APIs to trigger Android intents, upon calling related APIs. For example, using the code snippet above, when Python GPS API is executed, the API will send request to Android GPS intent via the RPC call. Once Android intent receives the request, it will grab the GPS data, and passes the GPS data back to Python GPS API through the same channel.

Lessons from the Trenches: rewriting and re-releasing virtualenv

2020-07-23T00:00:00+00:00

Description

virtualenv is a tool that builds virtual environments for Python. It was first created in September 2007 and lived most of its life being a single file project with an increasing amount of (scary) workarounds within. It managed to grow until it was 2,700 lines of code. Maintaining this project became increasingly more troublesome, to the point where, we had more than 500 open issues at one point. In July 2019, I started working from scratch on a rewrite, with the goal of not just increasing the project's maintainability, but also to make it faster and add some new features that were just impossible or too hard to do in the existing code base. Fast forward six months to January 2020, when we released the first beta, with the first full release coming out on 10th February. It took a bit more than a month to squash all the open bugs tickets, but April started without any remaining open bug tickets. This talk will cover the lessons I've learned while on this journey.

Speak Python with Devices

2020-07-23T00:00:00+00:00

Description

How Python can be used in IoT/infrastructure automation tasks

The talk will be a getting start guide on controlling hardware devices with Python. We know Python users are very keen on multitasking and always wish to know more about how it can be used in different tasks. This talk will help audiences exploring new Python skillset. Audiences may be inspired by this talk and apply it to many scenarios, e.g., IoT and infrastructure automation.

Intended audiences include: 1. wish to know how Python can be used beyond data analysis and web dev 2. a Pythonista who interested in craft some touchable things 3. want to acquire something new into your Python skillset

Audiences are expected to have basic knowledge about: 1. Python syntax and control flow 2. Computer and operating system (especially UNIX)

After this talk, audiences will have: 1. The basic idea of controlling devices with Python 2. Expanding their Python skillset. Know how to use Python in another interesting and useful task besides ML, web scrapping, etc.

Static Typing in Python

2020-07-23T00:00:00+00:00

Description

Python is well-known as a programming language without static types. This means that you don’t need to say what a given variable will hold, or whether your function will return a string or an integer (or sometimes one, and sometimes another!). This has historically made Python a very flexible and beginner-friendly language.

In this talk, we’ll discuss the advantages and disadvantages to a static type system, as well as recent efforts to introduce static typing to Python via optional “type hints” and various tools to aid in adding types to Python code. We’ll see what this means for Python, for Python programmers, and what the future has in store for Python’s type system.

Writing and Scaling Collaborative Data Pipelines with Kedro

2020-07-23T00:00:00+00:00

Description

How to get your Data Scientists and Data Engineers to play nice, both now and in the future.

The goal of this talk is to introduce data pipeline developers to QuantumBlack's approach for keeping data pipelines healthy and sustainable and facilitating collaboration between data scientists and data engineers by using our open source framework, Kedro. Attendees need between novice and intermediate knowledge of Python (enough to understand syntactic sugar and funargs) in order to appreciate this talk.

As data continues to inform more and more business strategy, high quality, fully featured data pipelines have never been more critical. Small data scripts and single-coder science projects are not enough to keep up with the pace of day-to-day business and their ever-growing list of requirements. Now, more than ever, we need data engineers and data scientists to collaborate effectively. Yet, these two parties come with inherently competing needs.

Data scientists need high data volatility and parameterization, for experimentation, and data engineers, on the other hand, need stability and performance, to deliver data. Furthermore, as pipelines grow, the cost of knowledge transfer and training new team members also increases. How can we get scientists and engineers to work well together, and sustain pipeline growth as the team also grows?

For this, QuantumBlack created Kedro, a framework for writing data pipelines that addresses both the needs for flexibility and stability in its features and patterns of use. By using Kedro’s tools and operating model, we have enabled our teams to scale our single-developer, micro-pipes to industrial sized data processors with dozens of developers; all without sacrificing readability, quality, or stability. This talk will show you how.

Escrevendo Testes com Flask e #JustPython

2018-08-25T00:00:00+00:00

Description

Escrevendo Testes com Flask e #JustPython - Palestra de Eduardo Mendes na Flask Conf 2018.

Death to the spinner: event sourcing for reactive web apps

2025-05-16T00:00:00+00:00

Description

Chris May shows how to use Flask, SSE, and Datastar to build a reactive UI with introspectable data, all with Jinja templates and a single codebase.

Let’s PyScript: Flask frontends in Python!

2025-05-16T00:00:00+00:00

Description

Nicholas Tollervey live codes a chat system with a Flask API and PyScript UI. He and Paul Everitt talk about how PyScript works, and then take Q&A from the audience through the newly created app. Presented at FlaskCon 2025.

Python’s New Template Strings and Flask

2025-05-16T00:00:00+00:00

Description

Dave Peck and Paul Everitt describe how their new PEP 750 t-strings template strings work. They demonstrate problems that t-strings solve, and show how they might be used to write a new template system or enhance an existing one. Presented at FlaskCon 2025.

Testing Flask and Quart apps with Playwright

2025-05-16T00:00:00+00:00

Description

Pamela Fox shows how Playwright can be used to write and run tests against a Flask/Quart app in a browser. Presented at FlaskCon 2025.

Turning Data into an Interactive Artwork That Tells a Story

2025-05-16T00:00:00+00:00

Description

Diane Phan describes her love of movies, music, and programming, and how she combined those into a fun app to share and map music memories. The audience is invited to use the live app during the talk. Presented at FlaskCon 2025.

Python: A Medley of Programming Paradigms

2019-08-24T00:00:00+00:00

Description

Exploring Python's multiple programming paradigms - Imperative, Procedural, Functional and Object Oriented - and their applications through Python examples.

dask-image: distributed image processing for large data

2020-09-06T00:00:00+00:00

Description

Genevieve Buckley

https://2020.pycon.org.au/program/KHULAN

Image datasets are large, and becoming larger. The widely used benchmark dataset COCO (Common Objects in Context) contains 330,000 individual images. The average size of a single entry on the image database EMPIAR is over 1TB, and can easily reach several terabytes. Even where individual images are small enough to fit in-memory, many existing parallelization methods are difficult to scale seamlessly between a laptop and a supercomputing cluster. For instance, the python multiprocessing module is restricted to a single mode and can't take advantage of multiple compute nodes on a distributed supercomputing cluster.

We need easy ways to work with large image data. This talk introduces dask-image, a python library for distributed image processing. The target audience are python programmers currently using numpy and scipy with large array data, where the whole dataset cannot fit in memory or is close to that limit. It's for people who want to get started with parallel processing, either because they have large single-image data, or because they want to do batch processing applying the same analysis to many smaller images (sometimes known an embarrassingly parallel problem). The specific image analysis functions provided by dask-image are of broad interest to a diverse range of analysis applications including (but not limited to) video/streaming data, computer vision, and scientific fields including astronomy, microscopy and geosciences.

Specifically, this talk will cover:

An overview of the dask-image library
- Lazy image loading
- Image pre-processing functionality (convolutions, filters, etc.)
- Analysis of segmented images (distributed labeling, and measurements of those label regions)
Mixing in your own custom analysis functions (using dask delayed, map_blocks, and map_overlap)
A practical case study of a Python image processing pipeline

dask-image is open source, released under a BSD 3-Clause license, and can be installed using conda or pip. You can find the source code at https://github.com/dask/dask-image and the quickstart guide at https://github.com/dask/dask-examples/blob/master/applications/image-processing.ipynb

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 13:55:00 2020 at Curlyboi

DevOps for Data Science? - automate the boring stuff and leverage the OSS ecosystem

2020-09-06T00:00:00+00:00

Description

Dr. Tania Allard

https://2020.pycon.org.au/program/WMMVSQ

Does your work (either research, non-profit or industry-based) depend on Machine learning, Data Science or data-intensive analyses? Have you ever wished you could automate some of the boring stuff while adding extra robustness to your workflows so that you and your team can have greater confidence and work more efficiently?

In this talk, I will present the concept of MLOps (kind of DevOps for ML scenarios, also referred to as DataOps or AIOps) and how adopting these practices can improve your team's workflows. You will learn how to automate some tasks within the ML lifecycle: from data transformation to model training, testing and validation, and deployment — making your workflows not only more seamless but your entire work more reproducible, reliable, and robust. You do not need to be a DevOps engineer to benefit from these practices, but you can indeed leverage existing open-source tools and platforms to improve your Data Science workflows. For completeness, I'll show a live end to end example, integrating MLOps practices for Machine Learning - from data processing to model training, validation and deployment. I will highlight the essential tips and tricks for each of the involved stages. You will leave the talk with practical recommendations and examples to get you started on adopting MLOps practices.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Fri Sep 4 16:35:00 2020 at Curlyboi

Extracting data from Excel with Python

2020-09-06T00:00:00+00:00

Description

samuel oranyeli

https://2020.pycon.org.au/program/FAEAH3

How do you read data from Excel? How do you read data that has meaningful formats(rows or cells are colour coded)? Or pivot tables that need to be in tidy format? Or data with hierarchical formatting ? This talk will show how to use Pandas, Openpyxl, and some other make-life-easy tools to extract data from Excel into Python in a tidy form and proceed to the next phase of our data analysis.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Fri Sep 4 13:20:00 2020 at Curlyboi

Rube Codeberg Competition

2020-09-06T00:00:00+00:00

Technosolutionism and human rights

2020-09-06T00:00:00+00:00

Description

Lucie Krahulcova

https://2020.pycon.org.au/program/XWQNFM

Technosolutionism proves that we need a multidisciplinary approach to tech design. In this session we will use the latest examples of the COVID19 apps to illustrate how a solution can be technically perfect while being perfectly inappropriate as a solution to a given problem. We will work backwards from the location/data sharing/API changes which were happily adopted for covid apps to illustrate how/when those sorts of opportunities can be seized by governments and malicious third parties. We will cover the ways in which every dataset aggregated and generated is likely to be appropriated and abused. Why does that happen? How can we prevent that at the design stages? I will lean on my extensive history in the field to pull together examples where policy and agencies have grabbed at opportunities like this to seize additional power, and use it to highlight the urgency of addressing this issue as we go through the everyday. We can change the chaotic technosolutionism that happens by being better informed and better prepared to do our jobs with a critical eye.

The session should leave the audience with a broader understanding of the challenges posed by one-stop-shop-technosolutionism and the essential questions that we as participants in the tech space have a responsibility for baking into our thinking and decision making.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Fri Sep 4 09:15:00 2020 at Python 2

Why the float did it NaN?

2020-09-06T00:00:00+00:00

Description

Jakub Nabaglo

https://2020.pycon.org.au/program/RTT87N

Floating point numbers are designed to be magic. For most mundane tasks, your calculations ‘just work’, and you are not supposed to think too much about it.

From time to time though, the magic stops working. The maths that’s correct on a sheet of paper no longer gives the right results when done on a computer. Results of NaN or Inf, division by zero errors, and inaccurate answers have caused great frustration to many.

To see why this happens, we will look under the hood of floating point numbers. Their structure in memory, and how it affects the accuracy of different operations. This is particularly relevant to data science, which entails lots of number crunching.

We will see why subtraction is sometimes dangerous, why you should avoid multiplying probabilities, and why you should never invert a matrix. We will explore the tools Python, NumPy, and SciPy give us to avoid these pitfalls, such as LU decomposition and the LogAddExp trick.

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 12:45:00 2020 at Curlyboi

3 ways to test S3 in Python

2020-09-05T00:00:00+00:00

Description

Sanjay Siddhanti

https://2020.pycon.org.au/program/VXXUWE

Benefits of testing code that uses S3
In-depth code examples of three ways to test S3:

Mock out the S3 API responses using botocore stubs
Mock the S3 API using moto
Spin up a LocalStack S3 instance and test against that

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Python, PyCon, PyConAU, PyConline

Sat Sep 5 16:00:00 2020 at Python 2

A Reflection on Software Testing

2020-09-05T00:00:00+00:00

Description

Richard Jones

https://2020.pycon.org.au/program/MXZVY8

This is an opinionated talk about issues I have observed during my career around software testing, including our conversations about it, but also the many materials produced describing how it should be done. One of my inspirations for this talk is the realisation that in some ways we have lost our way with testing, losing sight of some of the very early wisdom.

I will talk about some of those conversations we have, the language we use, but I will also present some advice, with concrete examples, of how we all might improve how we approach and implement testing.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Sat Sep 5 13:00:00 2020 at Python 2

A sack full of angry snakes: Taming your python dependencies with Nix

2020-09-05T00:00:00+00:00

Description

Thomas Woolford

https://2020.pycon.org.au/program/USWEYZ

Scientists, Researchers and Software Engineering projects should be paying attention to the reproducibility of their results. The best way to guarantee that the project you build today will be the same when built next week is to exactly specify your dependency tree.

Imperative package management approaches (such as apt/pip/conda) provide few assurances that the package version you get today is the same version you'll get tomorrow, or that installing packages in a different order won't change the versions resolved. The problem gets even harder when you want to integrate with native extensions that rely on platform libraries and the ephemeral contents of /usr/share/.

In this talk I'll discuss how Nix package manager can provide a complete dependency management solution for python projects on Linux and Mac. Nix can install and track exact, reproducible versions of python packages and their transitive dependencies including platform libraries, down to the compiler version that created them. No more guessing why your teammate can trigger a bug that you can't reproduce.

Nix can use this dependency info to quickly get new teammates up and running with a full development environment, no worries about the setup instructions being out of date or incompatible with their chosen OS flavor. Finally, Nix can build your project in a sealed build environment that prevents your project sneakily including files that haven't been declared as dependencies. You can then confidently deploy these builds as minimal docker images, VM images, or directly via nix package caches.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Fri Sep 4 13:20:00 2020 at Obvious

A website controlled Lego Robot using the Raspberry Pi

2020-09-05T00:00:00+00:00

Description

Brad Nielsen

https://2020.pycon.org.au/program/TVFCVP

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Fri Sep 4 11:00:00 2020 at Floperator

All Hands on Deck – Handling Security Issues

2020-09-05T00:00:00+00:00

Description

Markus Holtermann

https://2020.pycon.org.au/program/U9GBBD

We live in a world of technology and engineering where almost everything around us requires software. Unfortunately, the software we use or build has bugs. While most bugs can "just" be fixed, there are these other types of bugs, called vulnerabilities. Vulnerabilities can be found in our own infrastructure, on customers' infrastructure, or — worse — around user data.

Sadly, we see reports of leaked personal data on a daily basis. And when it comes to the companies who just had data leaked, it is astounding how rattled and unprepared they are for the situation. In fact, a lot of companies are puzzled when someone external approaches them about a possible security issue publicly. They don't know how to react and often react in the worst possible way: denial. But it is also about issues that are found from within the company. Issues that may not directly affect personal information. There is more to do than telling customers there is a security release of some software.

IT security is a sheer endless topic to talk about. It is a mindset and a company culture that must be lived by each and everyone within a company. In this talk, I will point out what roles individual departments play. Because there are more questions to be answered than “how and when are customers informed about an issue and a corresponding solution”. Are details about the issue released, and if so, when, and will the details be released publicly or only to customers? How will a public outcry about an issue on social media be dealt with? Is the social media team equipped to handle the masses? Will the sales and marketing teams be able to handle a hesitant customers base? What legal implications does the issue have? Who coordinates, makes decisions, and stays on top all of these moving parts?

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 14:30:00 2020 at Python 2

At The Root : Wagtail + Gatsby + GitPod

2020-09-05T00:00:00+00:00

Description

Dawn Wages

https://2020.pycon.org.au/program/KBXD9E

Exploring if a GatsbyJS progressive web app generator frontend with a headless Wagtail backend could be a solution for small-sized clients and personal projects when I want to "whip something up" quickly without sacrificing design, performance/hosting costs, user or site editor experience (Can I have it all)? Gatsby (https://www.gatsbyjs.org/) is a open source framework based on React. Wagtail (https://wagtail.io/) is a Django CMS with LOTS of cool features for content creators, and "plays nicely with everything else in your tech stack" (source: Wagtail) because its built on and easily integrates with all uses of Python.

Why? Not only do I enjoy the new and shiny things as a consultant, both Wagtail and GatsbyJS are established frameworks and active communities that put considerable effort in providing a great product with a smooth developer experience. We enjoy the benefits of Django / Wagtail CMS, with a incredibly fast pre-fetched progressive web app with GatsbyJS. The goal is nice, fast and fun.

I am approaching this talk by walking through how I started out with minimal experience with GatsbyJS or GraphQL, and moderate experience with React and headless Wagtail to creating and deploying (Netlify) this app. I am not an expert in either technology, but was able to quickly navigate getting started as well as identify lots of resources in both communities to build out in a more complex experience.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Sat Sep 5 10:25:00 2020 at Floperator

Biometric Unsecurity

2020-09-05T00:00:00+00:00

Description

Carina C. Zona

https://2020.pycon.org.au/program/9L3NTY

Examples are drawn from a globally geographically diverse range of areas.

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 10:25:00 2020 at Python 2

Build your own data warehouse for personal analytics with SQLite and Datasette

2020-09-05T00:00:00+00:00

Description

Simon Willison

https://2020.pycon.org.au/program/73UK8X

Many data enthusiasts dream of analyzing their own personal data, but few find time to build their own pipeline for it. This talk will show you how to get started with personal analytics with the highest possible return on your invested effort.

SQLite is the ideal tool for building a personal data analysis pipeline: it's free, fast and widely supported. Each database is a single file on disk, so you don't need to set up a database server to start using it. Tools that import data into SQLite can be written in any programming language, and its JSON support means it can even ingest data that may not fit neatly in a standard relational database table.

Datasette is a Python application that provides an interface over SQLite. It lets you bookmark and queries in your browser and export the results as JSON and CSV. The Datasette plugin ecosystem has over 30 plugins that extend Datasette in different ways, adding visualization tools, alternative export formats and more.

I'll show how to combine SQLite, Datasette and some simple Python scripts to ingest personal data from multiple different sources and build a personal data warehouse for your digital life. Data sources will include:

Twitter
Facebook
Apple Photos
LinkedIn
Google (via Google Takeout)
Foursquare / Swarm
GitHub
Apple Health
23AndMe

Techniques that work for an individual can work for organizations too. I'll finish by showing how this approach to working with data can scale up to solving professional problems in addition to personal analytics.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Fri Sep 4 16:00:00 2020 at Curlyboi

Can you keep a secret?

2020-09-05T00:00:00+00:00

Description

Aaron Bassett

https://2020.pycon.org.au/program/UKBKTR

We've all been guilty of hard-coding secrets at some point. It's just a quick hack, and you'll definitely go back and tidy it up later. But then you forget, and it's all too easy to git push your API keys to GitHub.

This easy to make mistake could end up [costing you thousands of dollars](https://dev.to/juanmanuelramallo/i-was-billed-for-14k-usd-on-amazon-web-services-17fn), and with the [median time to discovery for a secret key leaked to GitHub being 20 seconds](https://www.ndss-symposium.org/wp-content/uploads/2019/02/ndss2019_04B-3_Meli_paper.pdf) you could end up compromised before you have a chance to correct your error.

In this talk, we'll look at techniques that you can use personally and within your development teams to properly store, share, and manage your secrets, as easily as possible, and most importantly without disrupting your workflow.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Fri Sep 4 12:45:00 2020 at Obvious

Cracking open the COVIDSafe

2020-09-05T00:00:00+00:00

Description

Yaakov Smith

https://2020.pycon.org.au/program/URD9ER

As health authorities raced against the clock to restrict the spread of the Novel Coronavirus, bringing cities to a standstill, software developers too were racing to build a technological innovation. Their solution was ⁎✧˚₊ an app ༚⁎✧⁺ that would mystically allow you once again to go to the footy, hang out in pubs, return to work, and re-open the economy.

With the Australian Government running an unprecedented campaign to get as many people as possible to download their particular app, what this app actually does is still widely misunderstood. Furthermore, the app is somehow supposed to be safe and protect your privacy, whilst still being useful enough to track you and provide that information to medical contact tracers. How can that even work?

In this talk we will: ‹ul› ‹li›discover what contact-tracing is and why it is vital to curb the spread of the coronavirus‹/li› ‹li›explore how technology can be used to assist in the contact-tracing process‹/li› ‹li›analyze the COVIDSafe mobile apps for iOS and Android,‹/li› ‹li›discuss why you should, or shouldn’t, install COVIDSafe, and how contact-tracing apps try to balance personal security and privacy with the health needs of the greater community‹/li› ‹/ul›

Those who attend this talk will learn about bluetooth-assisted contact tracing, exposure notification, and the shiny new new COVID-19 settings on their mobile phone

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 09:50:00 2020 at Python 2

Deceptive Security using Python

2020-09-05T00:00:00+00:00

Description

Gajendra Deshpande

https://2020.pycon.org.au/program/GGGTVY

Imagine you are passing through an unknown street at midnight and you find that some anti-social elements are following you. To save yourself from them you start running and look for a safe place to hide yourself. On the way, you will find a good person and request the person to help you. The person hides you in the secure place to protect you. When these anti-social elements visit a good person’s place and enquire about you, the good person misguides them and redirects them to some other place in order to protect you. This is exactly how deception works. In this analogy, YOU are the resources to be protected, anti-social elements are the hackers who want to gain access to the resources, and a good person is a deception technique that protects the resources from hackers by making them fall in the trap.

How we implemented deception tool in python using machine learning We designed a deception tool in python language using PyBRAIN package to model and mitigate XPath injection attacks for web services. It is known that XML can be used to store the data and this data can be queried using XPath query language. XPath as a query language, it has injection issues similar to SQL. To handle this issue, we proposed a solution, which uses count-based validation technique and Long Short-Term Memory (LSTM) modular neural networks to identify and classify atypical behavior in user input. Once the atypical user input is identified, the attacker is redirected to fake resources to protect the critical data. Our experiment resulted in over 90% accuracy in classification of input vectors.

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 13:20:00 2020 at Python 2

Decoding: programming beyond text files

2020-09-05T00:00:00+00:00

Description

Nick Moore

https://2020.pycon.org.au/program/LYRJGY

We'll explore the history of typewriters and find homoiconicity where you might not be expecting it.

We'll talk about structured data and explore the early visions of hypertext.

We'll look at some specific examples of unusual programming environments and consider how a fresh look at these ideas can be applied to modern development.

And finally we'll consider what a difference this could make to the work of programming.

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Python, PyCon, PyConAU, PyConline

Sat Sep 5 16:00:00 2020 at Curlyboi

Engineers to Data-Scientists with Python

2020-09-05T00:00:00+00:00

Description

James Schulte

https://2020.pycon.org.au/program/BAC9R3

This talk centers around an early stage start-up reaching the scale-up phase of growth and having to move from an ad-hoc collection of MATLAB scripts to a functioning Python analysis pipeline. All while transforming a team of mechanical engineers into a functioning software engineering and data-science team.

I will paint a picture of the work we do, and how the product was first created - including the wild selection of technologies, before talking about some of the challenges faced in migrating a large monolithic product to a modular codebase. I’ll discuss some the advantages and pitfalls of using Python, and how we trained non-software engineers to produce quality code.

The talk has something for everyone, whether you’re a tech-lead responsible for training & developing your team, a business leader in a scaling startup, or a junior engineer thinking about how to improve.

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Python, PyCon, PyConAU, PyConline

Sat Sep 5 11:00:00 2020 at Obvious

Ensemble-X: Your personal strataGEM to build Ensembled Deep Learning Models for Medical Imaging

2020-09-05T00:00:00+00:00

Description

Dipam Paul, Alankrita Tewari

https://2020.pycon.org.au/program/DUGUZZ

Almost every individual today in the field of Data Science would know about the concept of Ensemble Learning in ML (ideally, the last chapter we read in the Machine Learning pedagogy). However, it is to also note that very few literatures exists to ensemble Deep Neural Architectures. Therefore, this is where we step forward and propose an approach to solve (almost) any medical imaging problem with the means of our Ensemble approach. Our approach does not just "solve" medical imaging problems but help practitioners to build unique and seamless architectures that almost never goes wrong (at least, not on your good days).

Why use an Ensemble at all?

In this section, I will try to anticipate the common notions one would have while reading this proposal and try to address the same in advance.

Of course, there are brilliant pre-trained architectures available and building up a custom CNN architecture takes seconds today, right? Then, why take the extra headache of combining architectures after all?

I wanted to give you a solid reason why then I thought I'll give you three:

Attaining state-of-the-art accuracy:

To elucidate on this, let's recall a very famous (or, infamous) chapter from our old books on Elementary Statistics that is - Central Limit Theorem. This theorem gives us two very important implications:

There are two big implications of the Central Limit theorem:

Ensembles of many random processes/variables converge to Gaussian distributions. That’s why normal distributions are everywhere. When adding together random numbers, the variance of the sum is the sum of the variances of those numbers. Which, in the essence of Machine/Deep Learning, translates to the fact that when we combine n (number of) architectures together then the combined architecture let's say, x will at all times produce better performance or results on standard metrics that every individual model in the n-cluster of models.

A slight limitation:

Central Limit Theorem also states that no matter how many models one tries to Ensemble one can never reach the accuracy of 1 (or, 100).

But that's alright, I believe. We don't need to attain a full 100% accuracy to prove the reliability of a model, do we?

Furthermore, we have proven this hypothesis of ours of attaining state-of-the-art accuracy in our published works with regards to the same. Kindly, refer.

Model Diversity:

This is arguably the most important contribution of ours out of all three which gives an indication of the reliability of our approach. Our experiments (which are also included in our papers) also goes on to show how well the architecture performs on external unseen examples - images which are not even part of the dataset.

Why is this important?

Elementary. We are dealing with Medical data where there is a plethora of possibilities, complications and unique cases. Hence, we can NEVER be too sure. Therefore, this was one of the most instrumental steps we had to set in motion in order to know if it's actually working or not.

Future tangible implementation scope: Mobile App or Web App to be used in Clinics and Hospitals.

Tackling the problem of Model Over-fitting:

In this contribution, we try to mitigate the problem of over-fitting to the greatest extent possible WITHOUT employing techniques such as Cross-Validation. The reason behind taking the entire dataset at once is very simple: Let's say you are dealing with a Binary-Class data with a huge class imbalance problem. Cross-Validation will not let you manually select the number of images that goes into each split that you make from each individual classes. It is also important to mention that the randomisation process ain't so bad altogether however, there's always room for improvements and alternate doors can also lead to better destinations.

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Python, PyCon, PyConAU, PyConline

Sat Sep 5 13:00:00 2020 at Obvious

Ensuring Black voices matter: Why your voice assistant is racist, and what we can do about it

2020-09-05T00:00:00+00:00

Description

Kathy Reid

https://2020.pycon.org.au/program/99XMUR

By 2025, there will be over 8 million voice assistants in the world. They are found on your mobile phone, in your home, in your car, and over time, will be embedded in many cyber-physical systems across the world. At the same time, there are over 7000 languages spoken in the world - "living languages".

But voice assistants support just a fraction of these languages. Moreover, accents and diversity within a spoken language are not well handled by voice assistants. For example, African American voices are much less likely to be correctly recognised by the speech recognition algorithms used within voice assistants. And as we start to interact with systems using voice, we have a human desire to listen to voices we resonate with. Voices like us. For many people, there are no synthesised voices that reflect their heritage, language, and gender expression.

There are several techno-social reasons behind this state of affairs.

The intent of a commercial voice assistant is to make money. This drives technical development in certain ways, such as certain languages being seen as more lucrative than others, irrespective of the number of speakers of that language. For example, there is more voice assistant support for Icelandic, a language spoken by 314,000 people, than there is for Kiswahili, a language spoken by over 100,000,000 people in Eastern Africa. Why? Money.
The big tech companies behind voice assistants have typically poor gender and racial diversity in their talent pool. Diversity in developers leads to diversity in development.
The data used for training speech recognition and speech synthesis models often has racial and gender biases. These can stem from both selection bias, but also broader systemic issues of inequality, such as the use of voice assistant technology to gather data - and the affordability of both that technology and its pre-requisites, such as internet access.
Many languages are considered "low resource languages". This means they often don't have written transcriptions, which are needed to train machine learning models. Those creating transcriptions often face the "transcription bottleneck" - a workflow impediment that means the creation of resources consumes significant labour time.

There are many established and emerging open source tools - many in Python - and movements that individually are addressing aspects of this broader techno-social system. Together, they can effect change so that everyone, everywhere can be afforded the benefits of voice technology.

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Python, PyCon, PyConAU, PyConline

Sat Sep 5 14:05:00 2020 at Floperator

geospatial data and analysis is :exploding_head:

2020-09-05T00:00:00+00:00

Description

Gala

https://2020.pycon.org.au/program/QJTSEP

Here is my top level summary:

its hard for everyone, you are not alone.
snacks: - points, lines and polygons - projections (scream quietly into the void) - WKT - file types - "simple" calculations
pantry: - libraries - useful stuff on the internet - quick wins - other software - debugging
dinner: - interesting problems - other fun things

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 09:50:00 2020 at Curlyboi

Getting Over the Boring Stuff Quicker - Building a Semi-Automated Speech Audio Annotation Tool

2020-09-05T00:00:00+00:00

Description

Xin Liang

https://2020.pycon.org.au/program/MQWLLM

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Python, PyCon, PyConAU, PyConline

Sat Sep 5 11:35:00 2020 at Obvious

GitHub Actions & Python

2020-09-05T00:00:00+00:00

Description

Jack McKew

https://2020.pycon.org.au/program/MRUT7W

GitHub Actions are free-to-use, plug & play blocks of continuous integration / continuous delivery (CICD).

This talk will go into how GitHub actions help me maintain my open source Python packages (Pandas_Alive), and how I released my own GitHub Actions for automating the packaging of Python code to executables with PyInstaller.

Tutorial blog posts I've written:

https://jackmckew.dev/github-actions-for-cicd.html

https://jackmckew.dev/how-to-make-github-actions.html

GitHub Actions I've released:

https://github.com/marketplace/actions/pyinstaller-windows

https://github.com/marketplace/actions/pyinstaller-linux

https://github.com/marketplace/actions/python-interrogate-check

Open source Python projects that GitHub actions help me maintain:

https://github.com/JackMcKew/pandas_alive

This talk has a brief mention of animated graphs to do with the COVID-19 pandemic.

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Python, PyCon, PyConAU, PyConline

Sat Sep 5 11:35:00 2020 at Floperator

Graph Databases will change your (freakin') life

2020-09-05T00:00:00+00:00

Description

Elena Williams

https://2020.pycon.org.au/program/A878CA

Relational and NoSQL DBs have ruled the roost for a couple of decades now, but in real life there's more to data than just tables or key-pairs. Graph DBMS technology has been coming along for the last decade-or-so and is now quite mature. Everyone wants one, just ask a Fortune 500 company.

I mean: why have a table when you can have a knowledge graph? Why not be able to whip up a recommendations engine (or indeed a fraud detector) in a few minutes?

Graph databases store data in Graphs -- that is NOT chart-visualisation nor syntax standard on API layers (NOT a GraphQL talk), but per the paper written by the mathematician Euler in 1736: those data structures which are "nodes" connected by "relationships".

This talk will be a primer on what this all means, how they work and when they're a good idea to use. There will also be a demonstration and discussion about how to kick off with graph DBs driven by python, specifically for people who are familiar with conventional databases, but have never used Graph databases before, but might be curious.

See code sample: https://github.com/elena/graph-fun

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Python, PyCon, PyConAU, PyConline

Sat Sep 5 14:05:00 2020 at Python 2

Hacking Playable Ads: What REALLY are they?

2020-09-05T00:00:00+00:00

Description

Evan Kohilas

https://2020.pycon.org.au/program/8TA7ZA

Have you ever clicked the FREE GEMS button and been served an ad, only to find you're now trialing a game?

What are they? Are they running code? If they are, can we hijack them? If we can't, can we bypass, or even replace them?

And more importantly, what are they actually doing? Are you really playing a game? Or are they bitcoin miners in disguise?

Come along and join my adventurous curiosity as we learn to man in the middle these ads and discover what they're really about!

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 13:55:00 2020 at Python 2

How a major museum runs on Python

2020-09-05T00:00:00+00:00

Description

Greg Turner, Ali Haberfield, Simon Loffler

https://2020.pycon.org.au/program/UKZXNT

ACMI, the Australian Centre for the Moving Image, is the most visited museum of the moving image in the world. In 2019 we closed our doors, to reshape our Federation Square building in order to become more public-facing, and to house a major new permanent exhibition, The Story of the Moving Image.

As you might imagine, we have a lot of moving image to show, and a lot of fascinating objects to tell people about, all of which can be overwhelming to some audiences. That's why we designed and built a system called The Lens. Every visitor to the museum can pick up a Lens, which they use to collect objects and media to watch and explore in their own time. The Lens depends on a network of hundreds of Raspberry Pi devices to display media and interact with visitors, all running Opensourced Python code. All these devices need to be robust and maintainable in order to survive the 10-year lifespan of the exhibition.

In this talk, we'll give you a tour of the technology at ACMI, including our Internet-of-Things fleet and management tools, and XOS, the eXperience Operating System, which provides content and configuration to the devices.

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Python, PyCon, PyConAU, PyConline

Sat Sep 5 16:35:00 2020 at Curlyboi

How learning python helped me teach C in tertiary education

2020-09-05T00:00:00+00:00

Description

Shrey Somaiya

https://2020.pycon.org.au/program/73HFNJ

This talk is simply one about my experience of learning C from a python background

what python features helped me understand C better (control flow, algorithm and problem breakdown design, functions, etc)
what C features helped me understand python better (ie pointers in C helped me understand python lists, etc)
how I use my python knowledge to teach C at University

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 10:25:00 2020 at Floperator

I don't need friends, I can build my own

2020-09-05T00:00:00+00:00

Description

Sarah Levins

https://2020.pycon.org.au/program/E9AXJE

Chatbots have become commonplace in our lives. These days, you may find they are largely used as a form of customer service. But how does it make us feel when we know we’re talking to a program? A poor interaction can sour the user’s experience and put them off using a product.

There is an art to making programmed responses feel natural. It’s not just about replying to our questions with the correct answer. Chatbots need to be programmed with the same abilities humans have to communicate. They need to be able to listen, understand and remember. This gives them the context of the conversation. So where do you start?

In this talk i’ll cover some of the core concepts behind how web chatbots work, strategies used to train them to feel more human, and some Python tools that you can use to start building your own.

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Python, PyCon, PyConAU, PyConline

Sat Sep 5 11:00:00 2020 at Floperator

Interactive Mapmaking with Python

2020-09-05T00:00:00+00:00

Description

Sangarshanan

https://2020.pycon.org.au/program/DVV7W8

Introduction (2 mins)
- Who am I?
- Setting the expectations

Working with Geodataframes (5 mins)
- Reading Spatial data as a GeoDataframe
- The awesomeness of GeoDataframes (An overview of some of the amazing things you can do with a single line of code eg: Spatial joins, Manipulations and transformations)
- Now that we has established our love for geopandas lets map geodataframes

Let the Mapmaking begin (10 mins)
- Basic Non-Interactive maps with Geopandas and Matplotlib
- Creating basic interactive maps with Folium, Plotly & Kepler (GeoJSONPlots, Clustermaps, Heatmaps, H3gridmaps)
  
  Conforming geodataframes to a format accepted by these libraries (usually JSON or a list of lists)
  
  Setting the right parameters for different plots
  
  Jazzing up the maps with additional layers, custom styles, tooltips, popups, colors etc
  
  Spatiotemporal Maps with Kepler.gl + Jupyter for handling huge datasets with ease
  
  Can we make it any easier ?
- Why can't making interactive maps be as easy as gdf.folium.plot()?

Enter Geopatra (8 mins)
- The need for Geopatra (Could it be any Easier and Faster)
- Wrapping up popular mapmaking libraries for GeoDataframes
- Create interactive maps with just a single line
- Future work and Discussion :)

Github: https://github.com/Sangarshanan/geopatra

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 17:10:00 2020 at Curlyboi

Introducing Lambda Calculus with Python

2020-09-05T00:00:00+00:00

Description

Indranil Ghosh

https://2020.pycon.org.au/program/AL8WB3

We will be focusing on untyped lambda calculus and start by the simple definition of a function, that can be implemented with Python lambda. We will then discuss the concepts of abstraction and application in lambda calculus and then move on to understand what a substitution is and how can these be implemented in Python. We will finally study some combinators which are the basic building blocks of combinatory logic, before ending with encoding datatypes in lambda calculus and implementing the same with Python lambda. Datatypes we will be discussing are:

Boolean values like TRUE and FALSE
Logical operators like AND, OR and NOT
Church Numerals
Predicates
Recursions

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Python, PyCon, PyConAU, PyConline

Sat Sep 5 14:40:00 2020 at Python 2

Journey into the Deep World of Web Development

2020-09-05T00:00:00+00:00

Description

Andrew Kim

https://2020.pycon.org.au/program/RMY7LZ

Are you from a non-tech industry but want to learn web development? Have you tried doing the official Django tutorial and had no idea what was going on?

This talk is a story about how I learnt Django and Python coming from a non-tech industry, during the COVID-19 pandemic. I will cover tips for beginners on how best to get started with Django and Python.

I will be sharing about mistakes that I made, resources that I found useful, and how I created connections both online and offline.

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 16:00:00 2020 at Python 2

Making Codebases Easier to Learn and Maintain

2020-09-05T00:00:00+00:00

Description

Tennessee Leeuwenburg

https://2020.pycon.org.au/program/AT33MZ

Learning a new codebase, returning to an old one, or exploring a new part of a system can sometimes be harder than it needs to be. This talk outlines tips and tricks for writing code that is both easier to maintain and easier for other people to learn.

Topics covered will include:

Documentation: both written and in-code
Structure: how to organise code for readability and easy re-use
Expression: how to improve comprehensibility of code
Tests: how to maximise their usefulness

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Python, PyCon, PyConAU, PyConline

Sat Sep 5 14:05:00 2020 at Obvious

Model Selection with Python: An Introduction to Hyper Parameter Tuning

2020-09-05T00:00:00+00:00

Description

Patrick Robotham

https://2020.pycon.org.au/program/LYBU8S

The world of machine learning is like a restaurant that presents an incredibly complex menu. There are so many choices for modelling algorithms and settings that it's hard to keep up. This talk discusses strategies for handling these choices in a principled, simple manner.

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 14:30:00 2020 at Curlyboi

Of Django, PostgreSQL schemas, and your multi-million dollar idea

2020-09-05T00:00:00+00:00

Description

Lorenzo Peña

https://2020.pycon.org.au/program/CRSJXF

Although rough for the world, 2020 is proving a good year (and decade start) for Django. Boring batteries-included frameworks and established monolith makers are shining again, in a world where the hype-enchantment of JavaScript and micro-everything is starting to wear off and fall into a more mature position of tradeoffs. After 15 years of life, Django continues to prove solid, battle tested, and very capable of major undertakings in the web arena.

Over the last decade, the competence of the framework has extended to the SaaS world, through a number of packages that have emerged as viable options for making Django multi-tenant. PostgreSQL concept of _schemas_ has opened a new exploration space for multi-tenancy, by combining the power of RDBMS native isolation with the reduced costs of managing a single database.

However, nothing comes for free. The ease of using PostgreSQL schemas for multi-tenancy in Django comes at the expense of some challenges that must be overcome in the short, medium, and long term, if the project seeks to survive while gracefully escaling to success.

In this talk we will cover the major challenges of doing multi-tenancy in Django through PostgreSQL schemas. You will learn the pros and cons of the approach as you move from tutorial-grade projects to massive multi-tenant behemoths. By the end, you will be able to answer whether or not this is going to be a feasible approach for your next multi-million dollar idea.

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 17:45:00 2020 at Python 2

Oh no! I think my project is outgrowing my Jupyter notebook. How do I survive?

2020-09-05T00:00:00+00:00

Description

Lydia Peabody

https://2020.pycon.org.au/program/BAAES3

An example-focused discussion of the pitfalls and strengths of interactive Python notebooks. Topics up for discussion: Namespace pollution - what is it and why do I care? Speed - when might my notebook be holding me back? Pretty pictures - are there times when even for visuals I might skip the notebook? Transitions - I’m so comfortable using my notebook. How do I change in useful ways without grinding to a halt? Functional combinations - can there be a happy middle ground between notebooks and scripts?

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 11:00:00 2020 at Obvious

On a shoe-string and a t2.small: scaling on a [zero] budget.

2020-09-05T00:00:00+00:00

Description

Tom Eastman

https://2020.pycon.org.au/program/CNNG8R

When you work for a company, you probably have a decent-sized infrastructure budget. And when you're building a Django project, you have a lot of scope for scaling it up: Add more gunicorn workers! Split the async task workers into their own servers! Quadruple the RAM of the database instance! Beef up your Elasticache!

Okay, but what if you can't just throw your employers' money at it? What if, to keep your side project affordable, you bought a reserved instance for it just a week before it started to go viral? What if the number of users starts going up 50% per week and... doesn't... stop...

You're gonna have to get clever.

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 14:30:00 2020 at Obvious

Oops! I Became an Open Source Maintainer! 😱

2020-09-05T00:00:00+00:00

Description

Mariatta

https://2020.pycon.org.au/program/JZW83Q

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 09:15:00 2020 at Obvious

Opening Keynote: MirrorWorld

2020-09-05T00:00:00+00:00

Description

Heidi Waterhouse

https://2020.pycon.org.au/program/WAY9J7

This talk is for anyone who feels alone, sitting in front of a monitor, with a mask hung on the door to the outside. Technology can't solve all our problems, and we shouldn't try, but the collective action of aligned communities can change the world. What has the Python community done, and what can we work on doing next? Where should we spend our resources, and how should we agree on priorities? What is investing in our present and what is for our future?

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Python, PyCon, PyConAU, PyConline

Sat Sep 5 09:25:00 2020 at Curlyboi

Outbreak Outreach - How COVID broke our program for the better

2020-09-05T00:00:00+00:00

Description

Renee Noble

https://2020.pycon.org.au/program/J9P8FY

The Girls’ Programming Network has a well tested-formula for creating communities of girls and women centred around a passion for coding. But when pandemic strikes, is maintaining communities based around connecting with hundreds of like-minded people even possible? Is it even possible to run an online event that upholds our values? And will kids even want to sign up for more hours of online learning?

I’ll take you through how we devised a plan to keep the GPN spirit alive online, with a dynamic schedule of learning, socialising and fun! I’ll show you the tech that made it all possible, tell you the lessons we learnt along the way, and let you in on some of the things we’ll be keeping in the long term.

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 09:15:00 2020 at Floperator

Panel Discussion: Ethical AI - From talk to practice

2020-09-05T00:00:00+00:00

Description

Nigel Dalton, Fiona Milne, Arna Karick, Lizzie Silver

https://2020.pycon.org.au/program/FNTEDL

As the fabric of civic infrastructure becomes increasingly digitised and data-driven, and our lives are nudged in the direction of what is presented as optimal many times a day, there is an increasing awareness of the importance of ethical AI. Issues that have been called out in particular are: data privacy, the harm that algorithmic bias can have, and the negative repercussions of unchecked recommendation systems blindly maximising engagement.

That ethical AI has become part of the discourse of data and AI is a positive sign, but how do we move from just talk? Who is responsible? What can you do about these issues?

In this moderated panel, we'll hear from three experienced data science practitioners on how these issues play out in their professional lives, as well as their perspectives on how we can actively work towards more ethical AI.

We hope this discussion will provide a valuable backdrop upon which to reflect throughout the rest of the day as we cover a range of exciting topics in science, data, and analytics.

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 09:15:00 2020 at Curlyboi

Piping Rain in Python - Adventures in a minute latency Radar Pipeline

2020-09-05T00:00:00+00:00

Description

Tishampati Dhar

https://2020.pycon.org.au/program/ZKKE98

In Australia we are used to live radar images provided by Bureau on Meterology (BoM). This agency provides forecasting and myriads of other services and has around 500 million in Annual budget. This is not the case in developing countries like Kenya where the last operational weather radar instrument was seen some 30 years ago.

Frustrated with the lack of live radar while in Nairobi a couple of engineers put together a passive radar system with an SDR and an Intel NUC. The data from this system travels via S3 and DynamoDB from screen capture tools on the endpoint device. Eventually gets exposed via API's to web and mobile apps running on some EC2 boxes ( no fancy kubernetes cluster to see here) . There are also a couple of Cron Jobs acting as Twitter and WhatsApp bots for followers. All this scratching your own itch infrastructure has slowly improved in uptime to 70% and supports around 5000 regular users. The entire data pipeline and web applications are written as set of small Python Services using Flask, OpenCV and ImageMagick.

This talk will describe how the pipeline was put together incrementally with intermediate outputs and rapidly deployed using a basic git pull based workflow. Along the way we will enumerate the numerous gotchas including the excessive S3 List usage, power failures on sensor site and dogpiling Twitter bots. The budget for the operations remains 100's of dollars per month.

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Python, PyCon, PyConAU, PyConline

Fri Sep 4 10:25:00 2020 at Obvious

Practicality Beats Purity: The Zen of Python’s Escape Hatch?

2020-09-05T00:00:00+00:00

Description

Christopher Neugebauer

https://2020.pycon.org.au/program/VVNEZR

When you type “import this” into a Python interpreter, you get PEP-20, 20* aphorisms that describe what “Good” Python code looks like. Most of these are prescriptive, and have been used to settle countless arguments about how Python should grow, or how new ideas should be implemented.

But in these 20 aphorisms (which you may know as the “Zen of Python”), there are some inherent contradictions.

When “explicit is better than implicit” and “simple is better than complex”, what does it mean when being explicit means you must introduce complexity?

When “readability counts” and “special cases aren’t special enough to break the rules”, what does it mean when making code more readable presents a special case in your codebase?

The answer lies in one often-forgotten aphorism: “practicality beats purity”. When you can weigh practicality as a factor, you can decide that other factors are restrictive, and discard them as “impractical”. This idea shows up in many places in Python:

Type hints can help you to be explicit, but they introduce complexity. Focusing on practicality means you can adopt type hints only when you need them. Decorators are simple, but can be used to introduce ambiguity. In many cases, the practical benefits of decorators outweigh their potential for misuse.

But how far can the pursuit of practicality let you go? Does practicality let you treat the other 19 aphorisms as optional? Does practicality give you an escape hatch from the rest of the Zen of Python?

In this talk, we’ll look at how practicality has been a guiding principle in the evolution of Python. We’ll look at how to weigh practicality in the face of the other 19 aphorisms of PEP-20, and we’ll look at how focusing on practicality can guide you towards these other attributes of good Python.

(* per PEP-20, the Zen of Python contains “20 aphorisms, only 19 of which have been written down.”)

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Sat Sep 5 10:25:00 2020 at Curlyboi

Procedural Generation of Terrain

2020-09-05T00:00:00+00:00

Description

Suzanne Baxter

https://2020.pycon.org.au/program/KLALFC

Procedural generation is the process of programmatically creating data. Its value is in generating large quantities of similar but not identical assets such as textures and models. This talk will cover the topic of using procedural generation to create a landscape, which will give an illustration of the strengths of procedural generation and ways to overcome its weaknesses.

More specifically, the topics covered are: - using layered opensimplex noise to create a heightmap, as well as an overview of types of noise and the shortcomings of purely noise-based terrain - using image processing techniques to add mountain ranges in a manner more consistent with real-world geography than noise alone would allow - further enhancing the terrain by converting it from a heightmap to voxels, and then applying an erosion simulation

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Sat Sep 5 13:35:00 2020 at Floperator

Publishing well-formed Python packages

2020-09-05T00:00:00+00:00

Description

Julin S

https://2020.pycon.org.au/program/HWU9J7

#### Why Most of the well established projects follow some practices and has some mechanism in place to ensure better maintainability.

But we can find a good number of packages online which are being developed without an equivalent mechanism.

In this talk, I will mention some tools, which when used, can lay the foundation for a well formed package.

#### Well-formed package

A package whose source has some structure which makes testing and development more efficient and less error-prone.

Such a package also makes working with its source a more pleasant experience.

A well-formed package makes things easier for both the developer and the user.

But it is easier to get started with a structure than to maintain that form. Which calls for a mechanism to maintain that structure.

#### How

The source code should adopt a style and stick with it. A linting tool like pylint can help with it. Or you could use black.

Use type annotations along with tools like mypy.

Eliminate unused code with vulture.

Add tests to be sure that the different functionalities work as intended using tools like pytest or the built-in unittest. This will be extremely useful to be sure that further modifications do not break existing code.

Ensure proper test coverage with coverage.py After all, adding tests isn't everything. One got to make sure it reaches every nook and corner.

Automate tests with tox, nox, etc.

Package the project in tune with the future of packaging. setuptools, flit or poetry

And add some CI/CD.

Other tools like pre-commit, bumpversion, etc may also be used.

#### Intended audience

People who create and publish python packages. The presentation is from the package developer's perspective and not exactly about the innards of the different python packaging tools.

#### Pre-requisites

Basic Python programming. Git

## Tentative talk outline

Linting

Type checking

Eliminating dead code

Tests

Test coverage

Test automation

Packaging

CI/CD

Other tools

Future of packaging

#### Speaker info (anonymized):

Using Python for 5 years. Interested in text processing.

Published a few packages to PyPI using different packaging tools.

The proposed talk is derived from experience of creating them.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Sat Sep 5 13:00:00 2020 at Floperator

Python Emergency Remote Teaching

2020-09-05T00:00:00+00:00

Description

Fernando Masanori

https://2020.pycon.org.au/program/PWZLAJ

During the pandemic lockdown of COVID-19, we found a very different context from the usual: a) students with much more time available for learning b) many students who did not have a personal computer and could only access classes by cell phone c) difficulty to realistically assess learning. In this lecture we will present the real experiences in a traditional programming course given during the Covid-19 pandemic.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Fri Sep 4 09:50:00 2020 at Floperator

Python for Accessibility

2020-09-05T00:00:00+00:00

Description

Rory Preddy

https://2020.pycon.org.au/program/VP3D73

My life is a hilarious roller coaster of miss-intended programming bugs because at 4 foot tall and 50 kilograms I completely fall off your radar.

Why did my scale call me! Why does facial recognition see me as a child? These are all valid questions I often ask myself as I navigate my weird and different world. Have you heard the phrase “You have to be this tall for Micro-services”? well what about: “You have to be this tall to operate a mobile phone?”. I am finding it harder and harder to reach any button except for “#” and “9”.

Building accessibility into the planning stages of programming can eliminate barriers for participation and create an inclusive environment for people with disabilities. Programming for diversity serves as an unquestionable indicator that your software embraces the diversity of your users and cares about their safety and comfort.

Join me on a fascinating and thought-provoking look at how you can program for Accessibility with Python.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Fri Sep 4 16:00:00 2020 at Obvious

Queues - The Secret Sauce

2020-09-05T00:00:00+00:00

Description

Jeremy Rotstein

https://2020.pycon.org.au/program/NKWBA3

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Sat Sep 5 14:40:00 2020 at Floperator

Raising Heretics

2020-09-05T00:00:00+00:00

Description

Linda McIver

https://2020.pycon.org.au/program/DD7SGY

In this talk, I want to show you how Data Science Education is key to nurturing a rationally sceptical, creative, ethical, problem solving population who can save the world. I’m going to do that by looking at the problems we have in the Data Science and Technology communities today, and how those communities are shaping our world - problems and all. We’re then going to explore the issues with our current education system in more depth. There is no such thing as perfect data, yet we treat data with more reverence than it deserves. Our entire education system is built on the idea of being measurable, yet all too often “measurable” winds up being the opposite of “meaningful”. We’ll consider how we got here, and how we can create an education system that focuses on meaningful outcomes, and develops our students into rational, ethical heretics, All of these goals require us to get comfortable with the idea of uncertainty. To be prepared to challenge the status quo, query accepted wisdom, and even to question our own findings. We're going to talk about why uncertainty is important, and how we can get comfortable with it, especially in education. Why should you take my word for it? I'll show you how data science projects work, with case studies from my own teaching, and examples of units ADSEI has created right across the curriculum, from Humanities to STEM.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Fri Sep 4 12:45:00 2020 at Floperator

So what is this time thing anyway?

2020-09-05T00:00:00+00:00

Description

Samuel Bishop

https://2020.pycon.org.au/program/DDCVAM

This is a whirlwind tour of modern timekeeping. From the weird and wonderful timekeeping of astronomers, to what goes into the normal civil time you read on your watch. With fun stops through calendaring, atomic clocks, GPS, space probes, and more along the way! As programmers, we work with dates and times on a regular basis and while most of us will never come across a TAI timestamp, or convert from UTC to TCB, there is a rich history behind our modern timekeeping with all its wonderful acronyms. Regardless of your level of skill or familiarity with timekeeping, I hope you can get something out of this introduction to timekeeping, illustrated with Python where applicable.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Sat Sep 5 14:40:00 2020 at Obvious

Speeding up Your Docker Image Build

2020-09-05T00:00:00+00:00

Description

Vladislav Supalov

https://2020.pycon.org.au/program/7FP8AB

Are your Docker builds taking forever? Docker can be a valuable part of your tool belt, or a constant source of annoyance. This talk will walk you through frequent sources of slowness when building Docker images for Python projects, and ways how you can avoid or fix them. Let’s speed up an utterly slow Docker build together using the right high-level approach, easy to implement tricks and brand-new Docker features to speed up your build protect your coding flow from annoying delays.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Sat Sep 5 11:00:00 2020 at Python 2

Staircase: fast analysis with step functions

2020-09-05T00:00:00+00:00

Description

Riley Clement

https://2020.pycon.org.au/program/3TDS8K

This talk introduces the Staircase package – a new open source package with applications in data analysis. It aims to take the audience on a journey from basics to more advanced usage at a comfortable pace. A tutorial-esque style will be used to introduce fundamentals, followed by four examples abstracted from real world use cases.

If you work with timestamped data and enjoy a little bit of math, mixed with a terrible sense of humour, then this talk could be right up your alley.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Sat Sep 5 10:25:00 2020 at Obvious

Stop Writing Tests!

2020-09-05T00:00:00+00:00

Description

Zac Hatfield-Dodds

https://2020.pycon.org.au/program/TCT3GH

We often think of manual testing as slower and less effective than automated testing, but most test suites haven't automated that much! Computers can execute all our pre-defined tests very quickly - and this is definitely a good thing, especially for regression tests - but the tricky parts are still done by humans.

We select test cases (inputs) and check that the corresponding outputs make sense; we write functions that "arrange, act, and assert" for our tests; and we decide - or script via CI systems - which tests to execute and when.

So lets explore some next-generation tools that we could use to automate these remaining parts of a testing workflow!

PROPERTY-BASED TESTING helps you to write more powerful tests by automating selection of test cases: instead of listing input-output pairs, you describe the kind of data you want and write a test that passes for all X.... We'll see a live demo, and learn something about the Python builtins in the process!

CODE INTROSPECTION, and a handy templating tool, can help write tests for you. Do you need to know any more than which code to test, and what properties should hold?

ADAPTIVE FUZZING tools take CI to its logical conclusion: instead of running a fixed set of tests on each push, they sit on a server and run tests full-time... fine-tuning themselves to find bugs in your project and pulling each new commit as it lands!

By the end of this talk, you'll know what these three kinds of tools can do - and how to get started with automating the rest of your testing tomorrow.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Sat Sep 5 11:35:00 2020 at Curlyboi

Streamlit - Build interactive data dashboards quickly

2020-09-05T00:00:00+00:00

Description

Jaimin Khanderia

https://2020.pycon.org.au/program/RL8S3N

Streamlit is a framework that allows you to build interactive analytical apps very rapidly. It is gaining popularity among data scientists and analysts around the world as it helps them make a data dashboard that is elegant and aesthetically pleasing. In my presentation, I’ll briefly state what is Streamlit and how it is super useful for data scientists and analysts. I’ll then go over the basic Streamlit components and examples that demonstrate how easy it is to make an interactive data dashboard in Python. (Yes you heard it right, no JavaScript required :D) By the end of this talk, you’ll know the basics of Streamlit and would be able to build an interactive web application for data visualization and analysis with just a few lines of code in Python.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Fri Sep 4 11:00:00 2020 at Curlyboi

Sunday Lightning Talks (PyConline AU 2020)

2020-09-05T00:00:00+00:00

Description

Extremely Online Lightning Talks from the extremely online PyConline AU 2020. CONTENT WARNING: Swearing, discussion of genocide.

0:00 - Intro 🐈 1:40 - Tisham Dhar - Coding at Home with Kids 6:33 - Juan Morais - A bot that shares game development content in Spanish 11:11 - Peter Hall - Java is better than Python 16:57 - Evan Kohilas - How did we miss this? 21:28 - Kit Darko - why not throw my devices in the sea, and go live in a cave? 26:32 - Henry Walshaw - Calculating 5km of travel distance in Python 32:00 - Allan Whatmough - Meltano: open-source data pipelines 37:20 - Matt Cengia - Event Diversity, Inclusion, and Accessibility 40:32 - Keira Paterson - Why won't they just use password managers already!? 45:13 - Jason King - The king has entered the area 50:33 - Carl Karsten - My fav answer to "How can I program?" 56:10 - Anthony Agius - 5 minutes to explain 5 ways Facebook causes harm 1:01:28 - Jared Quinn - Take Full Control of Your Smart Home 1:06:52 - Rachel Bunder - Curly boi or cake?

Taking Django's ORM Async

2020-09-05T00:00:00+00:00

Description

Andrew Godwin

https://2020.pycon.org.au/program/SLXVBU

The quest to add "hybrid" asynchronous support to Django - where it can run both synchronous and asynchronous code - is a long one. Django 3.1 reached an important milestone with synchronous and asynchronous views, and now the next big step is to take a long, hard look at the thing that makes up over half the Django codebase: the ORM.

The ORM is gigantic, old, and complex - and has an API designed and tweaked over many years. We'll look at some of those design decisions and how they reflect in the world of async, the challenges that underlie a hybrid API, as well as how the safety-first nature of the ORM has to evolve to deal with new and exciting async ways of breaking things.

We'll also dive into what it means to have asynchronous database backends, and how support for those are progressing in the Python world - and how we're trying to ship something that's useful before fully asynchronous database APIs are done.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Fri Sep 4 17:10:00 2020 at Python 2

Text files full of punctuation? There must be a better way to code.

2020-09-05T00:00:00+00:00

Description

Katie Bell

https://2020.pycon.org.au/program/HHPFHW

The vast majority of code is written through some kind of text editor, whether it's notepad, a sophisticated IDE, or a heavily customised Vim environment. These tools mitigate common text editing problems like unclosed brackets and syntax errors, but can't eliminate them completely.

In this talk we'll explore the underlying nature of code as a tree structure using Python's abstract syntax trees, and we'll compare real world examples of coding interfaces which represent these trees in different ways.

So much of our thinking and tooling revolves around code being represented as text files that to step outside of this world means re-imagining how version control works and what real programming even is.

Produced by NDV: https://youtube.com/channel/UCQ7dFBzZGlBvtU2hCecsBBg?sub_confirmation=1

Python, PyCon, PyConAU, PyConline

Sat Sep 5 11:35:00 2020 at Python 2

PyVideo.org - Python

A Python Takeover

Summary

Description

Django productivity tips and tricks

Summary

Description

Snakes on a cloud: the OpenStack project

Summary

Description

Sqlkit: empowering database access

Summary

Description

Exploit your GPU power with PyCUDA (and friends)

Summary

Description

Debugging and profiling techniques

Summary

Description

Django: trucchi per migliorare la produttività

Summary

Description

Meteorology, Climate and Python: desperately trying to forget technical details

Summary

Description

Contents

Background:

Programmazione MapReduce in Python con Pydoop

Summary

Description

Python 3: the Next Generation (is here already)

Summary

Description

Python for High Performance and Scientific Computing

Summary

Description

Python(x,y): diving into scientific Python

Summary

Description

Sfrutta la potenza della GPU con PyCUDA (e compagni)

Summary

Description

Spotify and Python: love at first sight

Summary

Description

Creare videogames con Panda3D

Summary

Description

Python Performance: Past, Present and Future

Description

Python Standard Library, The Hidden Gems

Description

The soul of the beast

Description

Why

Who

Outline

Advanced pytest

Description

Introduction to Python and MongoDB

Description

Testing Microservices: fast and with confidence

Description

“When a biologist met Python”

Description

Accelerate your Deep Learning Inferencing with the Intel® DL Boost technology

Description

From days to minutes, from minutes to milliseconds with SQLAlchemy

Description

Python vs Rust for Simulation

Description

Python's Parallel Programming Possibilities - 4 levels of concurrency

Description

Advanced Infrastructure Management in Kubernetes using Python

Description

Attractive GUIs with PySimpleGUI

Description

Automate your tasks with Python and publish with Chat Apps

Description

Deceptive Security using Python